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Algorithms for matching partially labelled sequence graphs

Overview of attention for article published in Algorithms for Molecular Biology, September 2017
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Title
Algorithms for matching partially labelled sequence graphs
Published in
Algorithms for Molecular Biology, September 2017
DOI 10.1186/s13015-017-0115-y
Pubmed ID
Authors

William R. Taylor, William R. Taylor

Abstract

In order to find correlated pairs of positions between proteins, which are useful in predicting interactions, it is necessary to concatenate two large multiple sequence alignments such that the sequences that are joined together belong to those that interact in their species of origin. When each protein is unique then the species name is sufficient to guide this match, however, when there are multiple related sequences (paralogs) in each species then the pairing is more difficult. In bacteria a good guide can be gained from genome co-location as interacting proteins tend to be in a common operon but in eukaryotes this simple principle is not sufficient. The methods developed in this paper take sets of paralogs for different proteins found in the same species and make a pairing based on their evolutionary distance relative to a set of other proteins that are unique and so have a known relationship (singletons). The former constitute a set of unlabelled nodes in a graph while the latter are labelled. Two variants were tested, one based on a phylogenetic tree of the sequences (the topology-based method) and a simpler, faster variant based only on the inter-sequence distances (the distance-based method). Over a set of test proteins, both gave good results, with the topology method performing slightly better. The methods develop here still need refinement and augmentation from constraints other than the sequence data alone, such as known interactions from annotation and databases, or non-trivial relationships in genome location. With the ever growing numbers of eukaryotic genomes, it is hoped that the methods described here will open a route to the use of these data equal to the current success attained with bacterial sequences.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 75%
Student > Ph. D. Student 1 25%
Readers by discipline Count As %
Social Sciences 2 50%
Physics and Astronomy 1 25%
Agricultural and Biological Sciences 1 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 September 2017.
All research outputs
#9,459,663
of 11,841,124 outputs
Outputs from Algorithms for Molecular Biology
#118
of 181 outputs
Outputs of similar age
#198,038
of 270,276 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 6 outputs
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So far Altmetric has tracked 181 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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